Analyst Guides & How-Tos

How to Set Up a SQL Database (Free, No Server)

Turn a CSV file into a real SQL database in about 15 minutes with free tools. Written for complete beginners.

How to Handle Large Datasets (Files Too Big for Excel)

What to do when a dataset is too big to open: use a database, index the key, sample first, and watch for the quirks that trip everyone up.

Git and GitHub for Analysts

The everyday Git loop, starting a repo, publishing with GitHub Pages, and fixing the five errors that catch everyone. For analysts, not software engineers.

COUNT in SQL, Explained for Beginners

COUNT(*) vs COUNT(column) vs COUNT(DISTINCT) — what each one really counts, why NULLs matter, and how analysts use COUNT to verify migrations and audit data.

The SQL CASE Expression, Explained for Beginners

CASE WHEN, THEN, ELSE, END — how SQL's if/else really works, taught with a real data-cleaning problem from Billboard chart history.

How SQL and Python Work Together

When do you need SQL, when do you need Python, and how do they hand off to each other? Explained with a real project that uses both.

Why Exploratory Data Analysis Matters

The detective phase of data work: why you must look at data before trusting it, backed by the classic research and shown on real projects.

Operationalization: Turning Fuzzy Questions Into Measurable Definitions

Every metric is a definition someone chose. How analysts build definitions they can defend — and why "what counts as an active user?" starts more arguments than any query.

Data-Driven Thresholds: Picking Cutoffs You Can Defend

"Why that number?" is the question every threshold must survive. The measure-price-defend method, plus the most dangerous equation in statistics.

Your Data Is Never Fully Clean: How Analysts Document Limitations

The professional standard isn't perfect data — it's documented, defensible imperfection. How limitations sections, quality logs, and quantified caveats work.